Abstract
Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these techniques are still far from practical application due to oversimplified settings. In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e.g., security. To excavate imperceptible changes in videos of the relay wall, we introduce difference frames as an essential carrier of temporal-local motion messages. In addition, we propose PAC-Net, which consists of alternating propagation and calibration, making it capable of leveraging both dynamic and static messages on a frame-level granularity. To evaluate the proposed method, we build and publish the first dynamic passive NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS datasets. NLOS-Track contains thousands of NLOS video clips and corresponding trajectories. Both real-shot and synthetic data are included.
Abstract (translated)
非可见性跟踪(NLOS)近年来吸引了越来越多的关注,因为它能够检测非可见的物体运动。以前的NLOS跟踪研究大多数依赖于 Active 照明,例如激光,并且成本高昂、实验条件复杂。此外,这些技术仍然远距应用于实际场景,例如安全。相反,我们提出了一种纯粹的 passive 方法,通过仅观察传递墙来跟踪一个人在一个看不见的房间里漫步,这更加符合实际应用场景,例如安全。为了挖掘传递墙视频中的微变化,我们引入了差异帧作为时间局部运动消息的重要载波。此外,我们提出了 PAC-Net,它包括交替传播和校准,使其能够在帧级别的 granularity 上利用动态和静态消息。为了评估提出的方法,我们建立了并发表了第一个动态 passive NLOS 跟踪数据集 NLOS-Track,该数据集填补了真实的NLOS数据集的空缺。NLOS-Track包含数千个NLOS视频片段和相应的轨迹。既有真实拍摄数据,也有合成数据。
URL
https://arxiv.org/abs/2303.11791
